Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

USBE: User-similarity based estimator for multimedia cold-start recommendation

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

To address user cold-start challenge in multimedia recommender systems, we proposed a new model named USBE in this paper. The model doesn’t take the new user’s personal and social information as the necessary parameters to solve cold-start challenge, and new user can complete cold-start by having a simple system experience. Based on the user-similarity and the discrimination of the multimedia items, the model can recommend suitable items for cold-start users and let users choose and give feedback independently. Our model is lightweight and low delay, and provides a new cold-start mode. To complement USBE model, we proposed a cyclic training multilayer perceptron model (Re-NN) to get the strategy of new user’s user-similarity changes. Experiments on a real-world movie recommendation dataset Movielens show: Our model has good results and achieves state-of-the-art after 4 rounds of cold-start recommendations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Algorithm 1
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data Availability

The data were constructed using the publicly available dataset Movielens 1M, and the construction method is described in the article. Movielens https://grouplens.org/datasets/movielens/

References

  1. Bobadilla J, Ortega F, Hernando A, Bernal J (2012) A collaborative filtering approach to mitigate the new user cold start problem. Knowl-Based Syst 26:225–238

    Article  Google Scholar 

  2. Cheng H-T, Koc L, Harmsen J, Shaked T, Chandra T, Aradhye H, Anderson G, Corrado G, Chai W, Ispir M, Anil R, Haque Z, Hong L, Jain V, Liu X, Shah H (2016) Wide & deep learning for recommender systems. In: Proceedings of the 1st Workshop on deep learning for recommender systems, pp 7–10

  3. Deldjoo Y, Kille B, Schedl M, Lommatzsch A, Shen J (2019) The 2019 multimedia for recommender system task: Movierec and newsreel at mediaeval. In: Mediaeval 2019 workshop

  4. Dureddy HV, Kaden Z (2018) Handling cold-start collaborative filtering with reinforcement learning, arXiv:1806.06192

  5. Gantner Z, Drumond L, Freudenthaler C, Rendle S, Schmidt-Thieme L (2010) Learning attribute-to-feature mappings for cold-start recommendations. In: 2010 IEEE International conference on data mining, pp 176–185

  6. Golbandi N, Koren Y, Lempel R (2011) Adaptive bootstrapping of recommender systems using decision trees. In: Proceedings of the fourth ACM international conference on Web search and data mining, pp 595–604

  7. Harper FM, Konstan JA (2016) The movielens datasets: History and context. Ksii Trans Int Inf Syst 5(4):19

    Google Scholar 

  8. He X, Liao L, Zhang H, Nie L, Hu X, Chua T-S (2017) Neural collaborative filtering. In: WWW ’17 Proceedings of the 26th International Conference on World Wide Web, pp 173–182

  9. Huang Z, Chen H, Zeng D (2004) Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. Acm Trans Inf Syst 22(1):116–142

    Article  Google Scholar 

  10. Jaiswal S, Virmani S, Sethi V, De K, Roy PP (2019) An intelligent recommendation system using gaze and emotion detection. Multimed Tools Appl 78(11):14231–14250

    Article  Google Scholar 

  11. Kula M (2015) Metadata embeddings for user and item cold-start recommendations. In: CBRecSys@RecSys, pp 14–21

  12. Kumar S, De K, Roy PP (2020) Movie recommendation system using sentiment analysis from microblogging data. IEEE Trans Comput Soc Sys PP(99):1–9

    Google Scholar 

  13. Lee H, Im J, Jang S, Cho H, Chung S (2019) Melu: Meta-learned user preference estimator for cold-start recommendation. In: Proceedings of the 25th ACM SIGKDD International conference on knowledge discovery & data mining, pp 1073–1082

  14. Lika B, Kolomvatsos K, Hadjiefthymiades S (2014) Facing the cold start problem in recommender systems. Expert Syst Appl 41(4pt.2):2065–2073

    Article  Google Scholar 

  15. Man T, Shen H, Jin X, Cheng X (2017) Cross-domain recommendation: an embedding and mapping approach. In: IJCAI’17 Proceedings of the 26th International joint conference on artificial intelligence, pp 2464–2470

  16. Mnih A, Salakhutdinov RR (2007) Probabilistic matrix factorization. In: Advances in neural information processing systems 20, vol 20, pp 1257–1264

  17. Qu Y, Cai H, Ren K, Zhang W, Yu Y, Wen Y, Wang J (2016) Product-based neural networks for user response prediction. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp 1149–1154

  18. Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J (1994) Grouplens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM conference on Computer supported cooperative work, pp 175–186

  19. Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web, pp 285–295

  20. Schein AI, Popescul A, Ungar LH, Pennock DM (2002) Methods and metrics for cold-start recommendations. In: SIGIR 2002: Proceedings of the 25th Annual International ACM SIGIR Conference on research and development in information retrieval, August 11-15, 2002, Tampere, Finland

  21. Schein AI, Popescul A, Ungar LH, Pennock DM (2002) Methods and metrics for cold-start recommendations. In: Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, pp 253–260

  22. Sedhain S, Menon AK, Sanner S, Xie L, Braziunas D (2017) Low-rank linear cold-start recommendation from social data. In: AAAI, pp 1502–1508

  23. Shan Y, Hoens TR, Jiao J, Wang H, Yu D, Mao J (2016) Deep crossing: Web-scale modeling without manually crafted combinatorial features. In: Proceedings of the 22nd ACM SIGKDD International conference on knowledge discovery and data mining, pp 255–262

  24. Shen J, Meng W, Yan S, Peng C (2013) Multimedia recommendation:technology and techniques. In: International acm sigir conference on research and development in information retrieval

  25. Volkovs M, Yu GW, Poutanen T (2017) Dropoutnet: Addressing cold start in recommender systems. In: Advances in Neural Information Processing Systems, Vol 30, pp 4957–4966

  26. Xiao J, Ye H, He X, Zhang H, Wu F, Chua T-S (2017) Attentional factorization machines: Learning the weight of feature interactions via attention networks. In: 26th International joint conference on artificial intelligence, pp 3119–3125

  27. Zheng G, Zhang F, Zheng Z, Xiang Y, Yuan NJ, Xie X, Li Z (2018) Drn: A deep reinforcement learning framework for news recommendation. In: WWW ’18 Proceedings of the 2018 World Wide Web Conference, pp 167–176

  28. Zhou K, Yang S-H, Zha H (2011) Functional matrix factorizations for cold-start recommendation. In: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, pp 315–324

  29. Zhou G, Zhu X, Song C, Fan Y, Zhu H, Ma X, Yan Y, Jin J, Li H, Gai K (2018) Deep interest network for click-through rate prediction. In: Proceedings of the 24th ACM SIGKDD International conference on knowledge discovery and data mining, pp 1059–1068

Download references

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant 61772449, and in part by the Natural Science Foundation of Hebei Province China under Grant F2019203120.

Author information

Authors and Affiliations

  1. Yanshan University, Qinhuangdao, Hebei, 066004, China

    Haitao He, Ruixi Zhang & Jiadong Ren

  2. Beijing Information Science and Technology University, Beijing, 100192, China

    Yangsen Zhang

Authors
  1. Haitao He
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ruixi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yangsen Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiadong Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ruixi Zhang.

Ethics declarations

Conflict of Interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

He, H., Zhang, R., Zhang, Y. et al. USBE: User-similarity based estimator for multimedia cold-start recommendation. Multimed Tools Appl 83, 1127–1142 (2024). https://doi.org/10.1007/s11042-023-15493-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-15493-9

Keywords

Search

Navigation